105 Open source projects that are alternatives of or similar to Quickml

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Machine learning for C# .Net

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

🏭 JavaScript Machine Learning Toolkit

simple tutorial on Machine Learning with Scikitlearn

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.

Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

R package "missRanger" for fast imputation of missing values by random forests.

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

gesture recognition toolkit

Machine Learning inference engine for Microcontrollers and Embedded devices

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

A python library for decision tree visualization and model interpretation.

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Machine Learning Lectures at the European Space Agency (ESA) in 2018

This is the implementation of Sparse Projection Oblique Randomer Forest

A python library to build Model Trees with Linear Models at the leaves.

A collection of research papers on decision, classification and regression trees with implementations.

Visualizes the Random Forest debug string from the MLLib in Spark using D3.js

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Handwritten Digit Recognition using Machine Learning and Deep Learning

A curated list of data mining papers about fraud detection.

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

文本分类基准测试

A New, Interactive Approach to Learning Data Science

A curated list of gradient boosting research papers with implementations.

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Generalized Random Forests

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Machine Learning library for the web and Node.

Text Classification Algorithms: A Survey

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

several methods for text classification

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Python code for common Machine Learning Algorithms

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Generative Forests in Python

A set of tools to understand what is happening inside a Random Forest

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

exploratory data analysis using random forests

This repo is intended to support replication and exploration of the analysis undertaken for our Urban Studies article "Understanding urban gentrification through Machine Learning: Predicting neighbourhood change in London".

YouTube Like Count Predictions using Machine Learning

I will update this repository to learn Machine learning with python with statistics content and materials

An end-to-end machine learning and data mining framework on Hadoop

InfiniteBoost: building infinite ensembles with gradient descent

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

随机森林，Random Forest（RF）

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.